1 Executive Summary

1.1 Overview

This study examines transport services and mobility challenges in Kumasi Metropolitan Area, Ghana, based on surveys of 393 passengers and 339 transport operators across four major routes.

1.2 Key Findings

2 Sample Characteristics

2.1 Passenger Demographics

Table 1: Passenger Demographic Characteristics (N=393)
Characteristic	Value
Sample Size	393
Female (%)	53.4%
Male (%)	46.6%
Mean Age Group	26–35 years (modal)
Tertiary Education (%)	23.7%
Self-employed/Trader (%)	30.3%
Income < GHC 1,000 (%)	19.6%
Long-term Residents (>7 years) (%)	69.2%

2.2 Detailed Demographic Breakdown

Table 2: Detailed Demographic Distribution
Group	Category	Count	Percentage
Gender
Gender	Female	210	53.4%
Gender	Male	183	46.6%
Age Group
Age Group	18–25 years	106	27.0%
Age Group	26–35 years	100	25.4%
Age Group	36–45 years	87	22.1%
Age Group	46–55 years	59	15.0%
Age Group	56 years and above	27	6.9%
Age Group	Below 18 years	14	3.6%
Education
Education	Junior High School (JHS)	92	23.4%
Education	No formal education	21	5.3%
Education	Primary education	35	8.9%
Education	Senior High School (SHS)	152	38.7%
Education	Tertiary (College/University/Polytechnic)	93	23.7%
Income
Monthly Income (GHS)	Below GHC 500	37	10.1%
Monthly Income (GHS)	GHC 1,000 – 1,499	66	18.1%
Monthly Income (GHS)	GHC 1,500 – 1,999	88	24.1%
Monthly Income (GHS)	GHC 2,000 and above	134	36.7%
Monthly Income (GHS)	GHC 500 – 999	40	11.0%

2.3 Driver Demographics

Table 3: Transport Operator Characteristics (N=339)
Characteristic	Value
Sample Size	339.0
All Male (%)	100.0
Age 26-45 years (%)	54.3
Basic Education (%)	36.3
Trotro Drivers (%)	54.3
>5 Years Experience (%)	49.6
Union Members (%)	66.4
Work Full Day (%)	69.9

3 OBJECTIVE 1: Range of Transport Services

To identify the range of transport services provided in the Kumasi Metropolitan Area

3.1 Transport Mode Distribution

Table 4: Primary Transport Mode Usage (N=393)
Transport Mode	Users	Percentage	% of Sample
Trotro (Minibus)	367	93.4	93.4%
Taxi	114	29.0	29%
Tricycle (Pragya)	73	18.6	18.6%
Motorcycle (Okada)	19	4.8	4.8%

3.1.1 Visualization: Transport Modes

3.2 Route-Specific Analysis

Table 5: Transport Service Distribution by Route
study_route	Sample (n)	% Trotro	% Taxi	% Tricycle	% Motorcycle	Avg Fare (GHS)	Modal Diversity
Ejisu– Kejetia Road	123	90.2	17.9	7.3	0.8	6.6	2
Sofoline – Adum Road	107	98.1	55.1	15.0	5.6	15.6	3
Adum- Tafo Road	87	100.0	16.1	27.6	10.3	7.9	4
Kejetia– Santasi Road	76	84.2	25.0	31.6	3.9	8.8	3

3.2.1 Visualization: Routes and Modes

3.3 Travel Purpose Analysis

Table 6: Primary Travel Purposes
Purpose	Count	Percentage	% of Trips
Work/Business	295	75.1	75.1%
Shopping/Errands	155	39.4	39.4%
Social/Religious	111	28.2	28.2%
School/Education	63	16.0	16%

3.4 Transport Frequency

Table 7: Weekly Public Transport Usage Frequency
Weekly Frequency	Count	Percentage	Cumulative %
1–2 times	63	16.0	16.0
3–5 times	140	35.6	51.7
6–7 times	81	20.6	72.3
More than 7 times	109	27.7	100.0

4 OBJECTIVE 2: Service Quality Indicators

2.To determine the indicators for assessing the quality of transport services

4.1 Service Quality Index (SQI)

Table 8: Service Quality Index (SQI) Summary Statistics
Statistic	Value
Mean SQI	2.37
SD	0.45
Median	2.40
Min	1.00
Max	3.20
% Excellent (≥3.5)	0.00
% Good (2.5-3.49)	41.98
% Fair (1.5-2.49)	52.67
% Poor (<1.5)	5.34

4.2 Quality Dimensions

Table 9: Service Quality Dimensions (Scale: 1=Poor, 4=Excellent)
Dimension	Mean Score	SD	Rating
Reliability	2.46	0.68	Fair
Affordability	2.46	0.65	Fair
Courtesy	2.43	0.78	Fair
Safety	2.37	0.71	Fair
Comfort	2.15	0.74	Fair

4.2.1 Visualization: Quality Dimensions

4.3 Reliability Analysis (Cronbach’s Alpha)

Table 10: Internal Consistency Reliability (Cronbach’s Alpha)
Statistic	Value
Cronbach’s Alpha	0.636
Raw Alpha	0.636
Standard Alpha	0.637
Average Correlation	0.260
Median Correlation	0.409
N Items	5.000

Notes: Cronbach’s Alpha >0.6 is considered acceptable for exploratory research. Inter-item correlations indicate moderate internal consistency among the 5 dimensions of the Service Quality Index.

4.4 Quality by Demographics

Table 11: Service Quality Index by Gender and Age Group
gender	age_group	n	Mean SQI	SD	95% CI Lower	95% CI Upper
Male	Below 18 years	7	2.60	0.40	2.30	2.90
Female	Below 18 years	7	2.54	0.56	2.13	2.96
Male	18–25 years	59	2.54	0.46	2.43	2.66
Male	36–45 years	39	2.46	0.36	2.35	2.58
Female	26–35 years	53	2.46	0.35	2.36	2.55
Male	26–35 years	47	2.43	0.44	2.31	2.56
Female	18–25 years	47	2.38	0.39	2.27	2.49
Male	56 years and above	7	2.29	0.47	1.93	2.64
Female	36–45 years	48	2.27	0.48	2.13	2.40
Female	46–55 years	35	2.20	0.46	2.05	2.35
Male	46–55 years	24	2.19	0.46	2.01	2.38
Female	56 years and above	20	2.02	0.56	1.78	2.26

4.4.1 Statistical Test: Gender Differences

Table 12: Independent Samples t-test - Service Quality by Gender
Group	Mean.SQI	t.statistic	p.value	Significance
Female	2.314	NA	NA	NA
Male	2.444	NA	NA	NA
Difference	-0.129	-2.855	0.005	**

4.5 Correlation Matrix

5 OBJECTIVE 3: Transport Service Challenges

3.To identify the challenges involved in using the existing transport services

5.1 Problem Frequency

Table 13: Most Common Transport Service Problems (N=393)
Problem	Count	Percentage	% Reporting	Rank
Overcrowding	253	64.4	64.4%	1
Long Waiting Times	234	59.5	59.5%	2
High Fares	192	48.9	48.9%	3
Reckless Driving	149	37.9	37.9%	4
Poor Road Conditions	97	24.7	24.7%	5

5.1.1 Visualization: Problem Prevalence

5.2 Problems by Transport Mode

Table 14: Transport Problems by Primary Mode
primary_mode	n	Long Waiting (%)	Overcrowding (%)	High Fares (%)	Reckless Driving (%)	Poor Roads (%)
Trotro	367	59.1	67.3	48.2	38.4	25.3
Taxi	18	77.8	16.7	50.0	27.8	16.7
Tricycle	6	33.3	33.3	83.3	50.0	16.7
Motorcycle	2	50.0	50.0	50.0	0.0	0.0

5.3 Chi-Square Tests

Table 15: Significant Problem-Mode Associations (Chi-Square Tests)
	Problem	Mode	Chi.Square	df	p.value	Significant
X-squared1	Long Waiting	Taxi	23.980	1	0	***
X-squared2	Overcrowding	Trotro	18.824	1	0	***

5.4 Logistic Regression: Overcrowding

Table 16: Logistic Regression - Predictors of Overcrowding Experience
Predictor	Odds Ratio	95% CI	p-value	Sig
age_group26–35 years	0.437	[0.21, 0.89]	0.024
monthly_incomeGHC 2,000 and above	0.363	[0.11, 1.08]	0.083
uses_trotro	9.049	[3.23, 28.7]	0.000	***
congestion_levelModerate	14.006	[2.35, 124.27]	0.007	**
congestion_levelSevere	9.845	[1.8, 81.99]	0.015
congestion_levelVery severe	11.658	[2.04, 100.15]	0.011

Model Performance: - Pseudo R² (McFadden) = 0.11 - AIC = 459.1

5.5 Delay Frequency Analysis

Table 17: Frequency of Transport Delays in Reaching Destination
Delay Frequency	Count	Percentage	Cumulative %
Rarely	27	6.9	6.9
Sometimes	163	41.5	48.3
Often	140	35.6	84.0
Always	63	16.0	100.0

6 OBJECTIVE 4: Mobility Challenges

To understand the mobility challenges among users of the current transport services

6.1 Congestion Analysis

Table 18: Traffic Congestion Severity Perception (N=393)
Congestion Level	Count	Percentage
Severe	192	48.9
Very severe	114	29.0
Moderate	76	19.3
Low	11	2.8

6.1.1 Visualization: Congestion Levels

6.2 Daily Traffic Time

Table 19: Time Spent in Traffic Daily (One-Way Commute)
Daily Traffic Time (One-Way)	Count	Percentage
Less than 30 minutes	98	24.9
30–60 minutes	168	42.7
1–2 hours	95	24.2
More than 2 hours	32	8.1

6.3 Peak Traffic Times

Table 20: Most Common Peak Traffic Delay Periods
Peak Traffic Time	Count	Percentage
Evening [4–8 pm]	190	48.3
Morning [6–9 am]	132	33.6
Afternoon [12–3 pm]	70	17.8
Late night	1	0.3

6.4 Vulnerable Groups

Table 21: Groups Perceived to Face Greatest Mobility Challenges
Group	Count	Percentage	Rank
Workers	269	68.4	1
Students	227	57.8	2
Traders/Market Women	188	47.8	3
Elderly	115	29.3	4
Persons with Disabilities	111	28.2	5

6.4.1 Visualization: Vulnerable Groups

6.5 Multiple Linear Regression: Travel Time

Table 22: Linear Regression - Predictors of Daily Travel Time
Predictor	Beta	Std. Error	95% CI	t-value	p-value	Sig
age_group56 years and above	0.343	0.179	[-0.009, 0.696]	1.913	0.056
occupationStudent	-0.277	0.130	[-0.532, -0.022]	-2.135	0.033
congestion_numeric	0.413	0.052	[0.31, 0.515]	7.940	0.000	***
study_routeEjisu– Kejetia Road	0.446	0.119	[0.212, 0.68]	3.751	0.000	***
study_routeKejetia– Santasi Road	-0.442	0.127	[-0.692, -0.191]	-3.468	0.001	***
uses_taxi	0.173	0.097	[-0.018, 0.365]	1.780	0.076
fare_per_trip	0.019	0.006	[0.007, 0.031]	3.201	0.001	**

Model Summary: - R² = 0.344 - Adjusted R² = 0.315 - F-statistic = 11.59 (p < 0.001)

6.6 Ordinal Logistic Regression: Service Quality

Table 23: Ordinal Logistic Regression - Predictors of Service Quality Rating
Predictor	Odds Ratio	95% CI Lower	95% CI Upper	p-value	Sig
age_group26–35 years	1.916	0.993	3.695	0.052
age_group36–45 years	2.750	1.328	5.694	0.006	**
age_group46–55 years	2.035	0.915	4.525	0.081
education_levelTertiary (College/University/Polytechnic)	2.481	1.306	4.712	0.006	**
monthly_incomeGHC 1,000 – 1,499	0.246	0.099	0.613	0.003	**
monthly_incomeGHC 1,500 – 1,999	0.251	0.101	0.622	0.003	**
monthly_incomeGHC 2,000 and above	0.179	0.072	0.448	0.000	***
monthly_incomeGHC 500 – 999	0.429	0.162	1.137	0.089
congestion_levelVery severe	0.142	0.034	0.589	0.007	**

Interpretation: Odds Ratios > 1 indicate increased likelihood of higher quality rating.

]

7 ADVANCED ANALYSIS: Machine Learning

7.1 Data Preparation

## ML Dataset prepared: 365 complete cases

7.2 Random Forest: Mode Switching

Table 24: Random Forest Performance - Mode Switching Prediction
	Metric	Value
Accuracy	Accuracy	0.731
Sensitivity	Sensitivity	0.723
Specificity	Specificity	0.738
Precision	Precision	0.680
F1	F1-Score	0.701

7.2.1 Feature Importance

7.3 K-Means Clustering: Passenger Segmentation

Table 25: Passenger Segment Profiles (K-Means Clustering)
cluster	Size (n)	% of Sample	Avg Fare (GHS)	Quality Index	% Long Waiting	% Overcrowding	% High Fares	% Trotro Users
1	120	30.5	7.0	2.5	27.5	100.0	24.2	99.2
2	116	29.5	7.7	2.5	41.4	0.0	33.6	84.5
3	102	26.0	8.1	1.9	96.1	87.3	85.3	96.1
4	55	14.0	23.1	2.6	100.0	80.0	67.3	94.5

7.3.1 Visualization: Cluster Profiles

7.4 Principal Component Analysis

Table 26: Principal Component Loadings (First 3 Components)
Variable	PC1	PC2	PC3
afford_score	-0.416	-0.556	0.119
reliability_score	-0.474	-0.391	0.358
comfort_score	-0.422	0.502	0.523
safety_score	-0.455	0.529	-0.215
courtesy_score	-0.466	-0.078	-0.733

Variance Explained: - PC1: 40.9% - PC2: 22.2% - PC3: 15.9% - Cumulative: 79%

8 DRIVER ANALYSIS

8.1 Driver Demographics & Operations

Table 27: Transport Operator Operational Characteristics
Metric	Value
Total Drivers	339.0
Avg Experience (years)	6.1
Union Members (%)	66.4
Full-time Operators (%)	69.9
High Daily Trips (7+) (%)	54.3
Poor Road Conditions (%)	42.5
Frequent Congestion (%)	73.7
Encounters Authority Challenges (%)	19.8

8.2 Congestion Causes (Driver Perspective)

Table 28: Congestion Causes Identified by Drivers (N=339)
Cause	Count	Percentage	Rank
Driver Indiscipline	270	79.6	1
Poor Traffic Control	265	78.2	2
Street Trading/Hawking	176	51.9	3
Poor Road Network	123	36.3	4
Lack of Parking Space	117	34.5	5

8.3 Text Mining: Authority Challenges

Table 29: Top 20 Words in Authority Challenge Descriptions
	Word	Frequency
extortion	extortion	11
police	police	7
always	always	6
asking	asking	6
drivers	drivers	6
money	money	6
stops	stops	6
unwarranted	unwarranted	6
bribe	bribe	5
collection	collection	5
arguments	arguments	4
fines	fines	4
harassment	harassment	4
officers	officers	4
sometimes	sometimes	4
taking	taking	4
unapproved	unapproved	4
verbal	verbal	4
collecting	collecting	3
fees	fees	3

8.3.1 Word Cloud

8.4 Improvement Suggestions

Table 31: Driver Suggestions for Mobility Improvement
Suggestion	Count	Percentage	Rank
Better Traffic Management	299	88.2	1
Enforcement of Traffic Laws	291	85.8	2
More Parking/Loading Spaces	271	79.9	3
Construction of New Roads	115	33.9	4
Reduction of Street Hawking	101	29.8	5

9 COMPARATIVE ANALYSIS

9.1 Driver vs Passenger Perspectives

Table 32: Comparative Challenge Perspectives
Challenge	Driver_Pct_Driver	Passenger_Pct_Passenger
Poor Roads	36.3	24.7
Indiscipline	79.6	0.0
Street Trading	51.9	0.0
Poor Traffic Control	78.2	0.0
Long Waiting	0.0	59.5
Overcrowding	0.0	64.4
High Fares	0.0	48.9

9.1.1 Visualization: Perspective Comparison

9.2 Route Comparison

Table 33: Comparative Route Analysis
study_route	Driver Sample	% Always Congested	% Poor Roads	Passenger Sample	Avg Fare (GHS)	% Severe Congestion
Adum - Tafo Road	124	50.8	62.9	87	7.9	89.7
Ejisu - Kejetia Road	117	16.2	31.6	123	6.6	76.4
Kejetia - Santasi Road	96	55.2	28.1	76	8.8	61.8
Sofoline - Adum Road	2	0.0	100.0	107	15.6	81.3

10 POLICY RECOMMENDATIONS

10.1 Infrastructure Preferences

Table 34: Infrastructure Improvement Priorities
Improvement	Support_Count	Support (%)	Priority
Expand Road Networks	269	68.4	High Priority
Improve Traffic Management	230	58.5	Medium Priority
Develop Bus Rapid Transit (BRT)	188	47.8	Medium Priority
Build Pedestrian Walkways	140	35.6	Lower Priority

10.2 Modern Transport Systems Support

Table 35: Public Support for Modern Transport Systems (Light Rail, BRT)
Response	n	Percentage
Do Not Support	57	14.5
Support	336	85.5

Transport Services and Mobility Challenges in Kumasi Metropolitan Area

Laurens

November 15, 2025